Dynamic Data Encapsulation Method, Client and Server Based on Data Item Analysis

Abstract

Embodiments of the present invention provide a dynamic data encapsulation method based on data item analysis, a client and a server. The method includes: it is characterized in that it includes: based on a given data item catalog and / or logical model catalog, acquiring data items and / or logical models selected by the user, and generating Query the data item set and the relational algebra operation tree to obtain the user's data query request; generate query logic LSQL according to the data query request, and send it to the server to obtain the data query result set; wherein, the query data item set includes The data item and / or logic model selected by the user; the relational algebra operation tree used to describe the query logic among the data items in the data item set. The embodiments of the present invention improve the response efficiency of data query requirements, reduce operation and maintenance costs, and at the same time better meet the data query requirements of users.

Description

technical field [0001] Embodiments of the present invention relate to the technical field of computer data encapsulation, and more specifically, relate to a dynamic data encapsulation method based on data item analysis, a client and a server. Background technique [0002] At present, many large enterprises such as communications, finance and other industries have already planned to build a big data platform and build a big data operation system within the enterprise, so as to integrate the data resources of various departments, subordinate organizations and business sectors within the enterprise, and serve the enterprise Business analysis and data visualization applications provide more comprehensive data services. In these enterprise-level big data operating systems, most of them still use the method of directly opening access to the big data platform to allow data applications to access the data resources of the big data platform. However, this method has great security r...

AI Technical Summary

Problems solved by technology

Due to the experience and ability limitations of OpenAPI developers, as well as the variety and continuous innovation of data applications, it is difficult for the OpenAPI provided by the data sharing platform to fully meet the data access requirements of data applications. Customized Data Access Requirements

[0010] Second, the response to data access customization requirements is slow and inflexible

If there are requirements changes or deviations in requirements understanding during this process, the business logic that has been developed and implemented needs to be modified, resulting in a longer waiting time

These factors often become one of the risks of out-of-control data application development progress

[0011] Third, it has high operation and maintenance costs

Existing technical solutions implement the business logic of data query and analysis in OpenAPI, so it is difficult for these OpenAPIs to be completely universal, and there are often customized OpenAPI requirements proposed by data application developers

Therefore, the managers of the big data operation system need to configure the OpenAPI development team. In addition to in-depth understanding of all data resources connected to the data sharing platform, team members also need to understand business requirements, develop and implement data query and analysis logic, and consume A lot of operation and maintenance costs

Images